This is a graphical representation of a hypothetical disease process within a population. It is hypothetical because it doesn’t specify the size of the population at risk or the actual length of time of observaton. This depiction also doesn’t account for loss-to-follow up or censoring. It assumes that all persons remain in the population at risk until removed by disease. The intent is to illustrate the fact that the population at risk of disease declines over time and eventually tapers off and plateaus at the number of persons who will never develop the disease. The rate of decline depends on the incidence of disease within the population.
In the opinion piece, the authors suggest a form of selection bias as a possible explanation for declining intervention efficacy in randomized trials. High risk individuals are removed from the population early on ultimately leaving only low risk people in the population who have a much lower incidence of disease at later time points.
This is a graphical representation of the change in incidence over time as suggested by the authors. Disease incidence is a measure of the proportion of a population who become infected during a specific time period (denoted by the blue lines). The numerator is the number of persons who become infected, or n. The denominator is the number of persons initially at risk, or N0.
This is a graphical representation of the change in incidence over time as suggested by the authors. Disease incidence is a measure of the proportion of a population who become infected during a specific time period (denoted by the blue lines). The numerator is the number of persons who become infected, or n. The denominator is the number of persons initially at risk, or N0.
Moving forward to an actual intervention scenario, assuming that the intervention is effective, the high risk group in the treatment arm will take longer to become infected because of the protection conferred by the intervention. However, they will still be among the first to become infected leaving the low risk group in the population at later time points.The incidence decline in the placebo group will be larger because the high risk subgroup will not benefit from the intervention.Over time, this incidence difference will gradually resolve.
As a result, the time-specific rate ratio will increase from a value of less than one to a value of one or greater. This process is referred to as “frailty”, “survivor bias”, “survivor cohort effect”, “crossing of hazards” or “depletion of susceptibles.”
As a result, the time-specific rate ratio will increase from a value of less than one to a value of one or greater. This process is referred to as “frailty”, “survivor bias”, “survivor cohort effect”, “crossing of hazards” or “depletion of susceptibles.”
As a result, the time-specific rate ratio will increase from a value of less than one to a value of one or greater. This process is referred to as “frailty”, “survivor bias”, “survivor cohort effect”, “crossing of hazards” or “depletion of susceptibles.”
Measuring the Potential Impact of Frailty on the Apparent Declining Efficacy in Randomized Trials of HIV Interventions: A Simulation Study
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“Measuring the Potential Impact of Frailty on the ApparentDeclining Efficacy in Randomized Trials of HIV Interventions: A Simulation Study” Felicia P. Hardnett Mathematical Statistician Quantitative Sciences and Data Management Branch
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Motivation Recent advancements in HIV prevention have given researchers hope that effective HIV interventions might soon become widely available Additional advancements in clinical trials methodology have also occurred to measure the efficacy of these interventions more accurately
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Problem The results of recent HIV intervention trials have been somewhat disappointing and difficult to explain The efficacy of the interventions appears to decline over time
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Recently Published Trials Two recently published trials (1 vaccine trial and 1 microbicide trial) concluded that intervention effectiveness decreased over time1,2. The investigators attributed this to: Waning vaccine efficacy (vaccine trial) Decreasing adherence (microbicide trial)1Abdool Karim Q, Abdool Karim SS, Frohlich JA, Grobler AC, Baxter C, Mansoor LE, et al. Effectiveness and safety of tenofovirgel, an antiretroviral microbicide, for the prevention of HIV infection in women. Science 2010; 329:1168–1174.2Michael N. RV 144 update: vaccination with ALVAC and AIDSVAX to prevent hiv-1 infection in thai adults. 17th conference onretroviruses & opportunistic infections, (2010).http://app2.capitalreach.com/esp1204/servlet/tc?c¼10164&cn¼retro&e¼12354&m¼1&s¼20431&&espmt¼2&mp3file¼12354&m4bfile¼12354.
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Alternative Explanation In addition to these phenomena, the authors of a recently published opinion piece assert that frailty (due to heterogeneity in infection risk) is another possible explanation1. This explanation is rarely cited in the literature as a possible explanation for declining efficacy.1O’Hagan JJ, Hernan MA, Walensky RP, Lipstitch M. Apparent declining efficacy in randomized trials: examplesof the Thai RV144 HIV vaccine and South African CAPRISA 004 microbicide trials. AIDS 2012, 26:123-126.
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The Potential Impact of Frailty Even if the efficacy of an intervention remained constant, frailty could give the appearance that its declining This could cause researchers to reject an effective intervention
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PurposeTo explore the potential impact of frailty on theresults of randomized trials of HIV interventions
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What is Frailty? Heterogeneity in infection risk within a study population Causes change in the composition of the study population over time Causes the measure of effect (risk ratio) to approach 1 over time
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Illustration of a hypothetical disease process within a population Population at risk# persons who neverdevelop disease Time • As people become infected, the population at risk decreases over time and eventually plateaus • The rate of decline depends on disease incidence • The curve plateaus at the number of persons who will never develop the disease (low/no risk people)
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Population at risk Illustration of Frailty as presented in the paper High risk Low risk Time The opinion piece asserts: • High risk individuals will be infected early on and will be removed from the population at risk first. • This will leave lower risk individuals in the risk population resulting in lower disease incidence at later time points.
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Graphical representation of disease incidence N0 High risk Population at risk (N) n1 Low risk n2# persons who neverdevelop disease Time t0 t1 t2 t3 Incidence=Number who become infected (n) From t0 to t1 Number intially at risk (N0)
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Graphical representation of disease incidence N0 Population at risk (N) n1 n2# persons who neverdevelop disease Time t0 t1 t2 t3 • Fewer cases diagnosed at a later time point because the high risk people are gone. • Incidence, therefore decreases.
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Intervention Scenario Population at risk Treatment arm Placebo RR=1# persons who neverdevelop disease Time • If the intervention is effective, it will prolong the time before high-risk individuals in the treatment arm will become infected. • Incidence decline in the placebo group will be larger because those at high risk will be quickly removed from the population at risk.
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Intervention Scenario Rate ratio= incidence (treatment arm) incidence(placebo) Population at risk Treatment arm Placebo RR=1# persons who neverdevelop disease Time • As a result, the time-specific rate ratio will increase from a value of less than one to a value of one or greater. • This process is termed “frailty”,“survivor bias”, “survivor cohort effect”, “crossing of hazards” or “depletion of susceptibles”.
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Intervention Scenario Rate ratio= incidence (treatment arm) incidence(placebo) Population at risk Treatment arm Placebo RR=1# persons who neverdevelop disease Time • As frailty increases, the curve becomes more steep early on and less steep towards the end. • RR approaches 1 sooner.
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Intervention Scenario Rate ratio= incidence (treatment arm) incidence(placebo) Population at risk Treatment arm Placebo RR=1# persons who neverdevelop disease Time • As frailty increases, the curve becomes more steep early on and less steep towards the end. • RR approaches 1 sooner.
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Possible Impact on RateMeasures This risk ratio comparing the incidence in placebo and treatment group becomes increasingly attenuated as follow-up time increases. This occurs even if risk factors were balanced between study arms at baseline and if effect of intervention is constant over time.
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Is Frailty Really Important?Based on the information presented in the paper:With competing factors such as waning vaccineefficacy and decreasing adherence, it’s not clearhow important frailty is in explaining decliningefficacy in the two trials.
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Current Study ObjectiveTo quantify the potential impact of frailty undervarious study scenarios.
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Current Study Approach We designed several study scenarios using study-related, intervention-related and population-related parameters. We held study-related factors constant (e.g., sample size, follow-up time, intervention effectiveness).
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Current Study Approach We varied population and intervention-related parameters (e.g. waning and frailty) We estimated the risk ratio at each time point for each scenario and quantified the change that is attributable to frailty.
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Modeling Description Definition of Parameters Model Assumptions Scenario Design Results Conclusions
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Modeling Description Definition of Parameters Model Assumptions Scenario Design Results Conclusions
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Model Parameters Study Intervention PopulationFixed • Sample Size Intervention Distribution of • Follow-up Efficacy Population Period across Risk • Number of Groups Risk GroupsVaried Waning Probability of -- Disease 25
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Risk GroupsThe study population is divided into mutually-exclusive groups ranging from very high risk to verylow risk.
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Modeling Description Definition of Parameters Model Assumptions Scenario Design Results Conclusions
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Model Assumptions Sufficient sample size The treatment arms have equal sample sizes. Disease risk is balanced between both treatment arms at the beginning of the study. Non-differential loss to follow up.
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Model Assumptions The intervention is effective at reducing the probability of disease and presents no adverse effects (i.e., increasing in the probability of infection) at any point in time. Intervention efficacy is constant across all risk groups. Intervention waning/non-adherence is constant across all risk groups.
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Modeling Description Definition of Parameters Model Assumptions Scenario Design Results Conclusions
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Features of Study Scenarios thatremain fixed Equal sample size in each treatment arm Ten-year follow-up time Five HIV risk groups ranging from very high risk to very low risk Intervention effectiveness - 50%
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Features of Study Scenarios thatremain fixed Distribution of study population across risk groups 0.6 0.5 0.4 0.3 0.2 0.1 0 Very High High Moderate Low Very Low
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Features of Study Scenarios thatare varied Waning- the rate at which the intervention loses its effectiveness Frailty- heterogeneity in disease risk across the 5 risk groups
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Modeling Description Definition of Parameters Model Assumptions Scenario Design Results Conclusions
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Conclusions With the exception of the most extreme cases, frailty (heterogeneity in disease risk) doesn’t appear to have much of an impact on outcome measures in randomized trials of HIV interventions The impact of frailty appears substantial in scenarios when HIV infection is a virtual certainty in the highest risk group and negligible in the lowest risk group
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Conclusions This study condition is unlikely to occur in most trials where higher risk individuals are commonly recruited. Therefore, frailty is less likely to explain a substantial portion of the declining efficacy in many HIV intervention trials
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